class_docstr: Similar to the official `egcn_h.py`. We only consider the node in a timestamp based subgraph,
  so we need to pay attention to `K` should be less than the min node numbers in all subgraph.
  Please refer to section 3.4 of the paper for the formula.
code: def forward(self, node_embs):
      scores = node_embs.matmul(self.scorer) / self.scorer.norm().clamp(
          min=1e-6
      )
      vals, topk_indices = scores.view(-1).topk(self.k)
      out = node_embs[topk_indices] * torch.tanh(
          scores[topk_indices].view(-1, 1)
      )
      # we need to transpose the output
      return out.t()
  
imports: import torch
  import torch.nn
  from dgl.nn.pytorch import GraphConv
  from torch.nn import init
  from torch.nn.parameter import Parameter
label_desc: Use image node_id 3 for calling the TopK obj's underlying member method code with example usage: obj.forward(node_embs) and returns: out